Generally described, computing devices utilize a communication network, or a series of communication networks, to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or provide services to third parties. The computing devices can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as a “data center,” may include a number of interconnected computing devices to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public.
Data centers or other computing devices may be utilized to process data intensive applications, such as machine learning applications. In one aspect, machine learning applications may generally describe applications that enable computing devices to define or modify behaviors based on empirical data. For example, a machine learning algorithm may be utilized to determine what rules best facilitate a medical diagnosis based on a number of tests, or to determine the likelihood that a credit card transaction is fraudulent. Generally, machine learning applications utilize a set of data, sometimes referred to as training data, to predict or evolve related decision algorithms. Training data may correspond to a set data points evaluated based on a set of rules. For example, training data may comprise a number of credit card transactions, a number of rules determining whether elements of a transaction are indicative of fraud, and the result of each credit card transaction when evaluated according to the rule. A machine learning application may utilize training data, in addition to other data, such as historical records of fraudulent transactions, to determine an algorithm for predicting fraudulent transactions. For example, a machine learning application may select an algorithm that incorporates a number of the rules within the training data, while not incorporating other rules. In some applications or environments, training data may require large amounts of memory to store, due to the volume of data points or rules included.